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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm

Trajectory Optimization of Manipulator for Loading and Unloa...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Dongliang Zhao Yiming Fang Chen Yang Jingliang Xin Key Lab of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao Hebei Province China Hebei Iron and Steel Group Tangshan Hebei Province China

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter.

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Multi-channel assignment in wireless sensor networks: A game theoretic approach

Multi-channel assignment in wireless sensor networks: A game...

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IEEE INFOCOM 2010

作者： Yu, Qing Chen, Jiming Fan, Yanfei Shen, Xuemin Sun, Youxian State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou 310027 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424458363

In this paper, we formulate multi-channel assignment in Wireless Sensor Networks (WSNs) as an optimization problem and show it is NP-hard. We then propose a distributed Game Based Channel Assignment algorithm (GBCA) to solve the problem. GBCA takes into account both the network topology information and transmission routing information. We prove that there exists at least one Nash Equilibrium in the channel assignment game. Furthermore, we analyze the sub-optimality of Nash Equilibrium and the convergence of the Best Response in the game. Simulation results are given to demonstrate that GBCA can reduce interference significantly and achieve satisfactory network performance in terms of delivery ratio, throughput, channel access delay and energy consumption. ©2010 IEEE.

关键词： Wireless sensor networks

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A data processing and analysis testbed for WSNs: Design and implementation

A data processing and analysis testbed for WSNs: Design and ...

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International Conference on Communications and Mobile Computing

作者： Cheng, Peng Cao, Xianghui Chen, Jiming Cao, Kejie Sun, Youxian Shen, Xuemin State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou 310027 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769543574

In this paper, we design and implement a reliable data processing and analysis test bed (NESCTB) for wireless sensor networks (WSNs). It contains 36 wireless sensor nodes connected through Ethernet to a central server so that, with web interface, remote users are able to schedule tasks and monitor the nodes' operations online. In addition, reprogramming and data logging can also be provided via the wired network. Powerful data processing and analysis tools are developed for information searching and processing, real-time topology visualization, 2-D and 3-D viewing of statistical diagrams, snapshots and timing sequences. The tools can significantly improve user-friendliness and operability of the test bed. The application scenarios of the test bed are discussed and a validation experiment for the referred application case NESCSurge demonstrates the convenience and effectiveness of NESCTB. © 2011 IEEE.

关键词： Visualization

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Experiments on autonomous mobile sensor control for target tracking

Experiments on autonomous mobile sensor control for target t...

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2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks", WoWMoM 2010

作者： Li, Hongbin Zhang, Fan Chen, Jiming Sun, Youxian State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou 310027 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424472659

Technology advancements in robotics and communication make it possible to combine mobility and static wireless sensor networks. Being able to locomote enables sensors to (1) adjust their positions for better sensing quality, (2) ensure field coverage by spreading out and (3) fill up communication gap to guarantee information sharing in wireless environment. Concerning the above three criteria, we propose a autonomous management scheme for mobile sensors, aims to achieve distributed sensor position control. Accordingly, we developed a testbed for mobile sensor network verification, with good interfaces to obtain ranging measurements and control sensor motion. Implementation details of building static and mobile sensors are given. Finally we present experiment results to verify the proposed sensor control scheme. ©2010 IEEE.

关键词： Target tracking

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On energy-efficient trap coverage in wireless sensor networks

On energy-efficient trap coverage in wireless sensor network...

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2011 32nd IEEE Real-Time Systems Symposium, RTSS 2011

作者： Li, Junkun Chen, Jiming He, Shibo He, Tian Gu, Yu Sun, Youxian State Key Lab. of Industrial Control Technology Zhejiang University China Computer Science and Engineering University of Minnesota United States Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Singapore

ISBN: (纸本)9780769545912

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of MinimumWeight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution. © 2011 IEEE.

关键词： Sensor nodes

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MPC-Based Trajectory Tracking control Strategy Incorporating Autonomous Lane-Changing Mechanism for Intelligent Vehicles

MPC-Based Trajectory Tracking Control Strategy Incorporating...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Dongxue Zhang Huiwen Liu Xiaohong Jiao Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATlab/Simulink co-simulation platforms.

关键词： Accuracy Intelligent vehicles Trajectory tracking Navigation Vehicle safety Trajectory MATlab Predictive control

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Backstepping sliding mode control for electro-hydraulic servo system with input saturation via an extended state observer

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Journal of Computational Information Systems 2014年第5期10卷 1955-1963页

作者： Shi, Shengli Li, Jianxiong Li, Yehong Fang, Yiming Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China College of Science Yanshan University Qinhuangdao 066004 China

The position tracking control scheme is investigated in this paper for electro-hydraulic servo system with parameter uncertainties and external disturbances. First, the total uncertainty is estimated via an extended state observer, and backstepping sliding mode control law based on the extended state is designed to ensure the system output track the reference signal. Then the control strategy for electro-hydraulic servo system subjected to input saturation is further considered. The extended state observer technique is applied to compensate the effects of the compound uncertainty, which contains not only the parameter uncertainties and external disturbances, but also the unknown input saturation. With the proposed method, the stability of the resulting closed-loop system is proven and the tracking errors converge to a small neighborhood of zero. Finally, simulation results are presented to verify the effectiveness of the proposed tracking control strategies. © 2014 Binary Information Press.

关键词： Backstepping

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Deep Learning based Defect Detection Algorithm for Solar Panels 5

Deep Learning based Defect Detection Algorithm for Solar Pan...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Li, Jiaqi Li, Hongxu Wu, Yifan Zhou, Hailiang Manfredi, Luigi Li, Peng Zhang, Hong Hebei Province Yanshan University Department of Key Lab of Industrial Computer Control Engineering Qinhuangdao066004 China University of Alberta Department of Computing Science EdmontonABT6G 2R3 Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. China United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350307320

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots. © 2023 IEEE.

关键词： Solar panels

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Introduction to time series search engine systems

Introduction to time series search engine systems

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2012 International Conference on Systems and Informatics, ICSAI 2012

作者： Zhang, Chuanlei Luo, Ji'An Zhang, Shanwen Zhang, Chen Department of Electrical and Computer Engineering Ryerson University Canada State Key Lab. of Industrial Control Tech. Zhejiang University Zhejiang 310027 China Department of Engineering and Technology Zhengzhou University China Discover Information Technology Limited Shanghai China

Basic structure of time series search engine system, involving acquisition, data processing and retrieval sub-systems, was elaborated. And options for time series search engine architectures and supported features and... 详细信息

ISBN: (纸本)9781467301992

关键词： Search engines

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Reinforcement Learning Combined with Heuristic Search for Solving Discrete Space Path Planning Problems

Reinforcement Learning Combined with Heuristic Search for So...

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第33届中国控制与决策会议

作者： Xiuling Zhang Xuenan Kang Kailun Wei Jinxiang Li Kai Ma Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Reinforcement learning(RL) has been successfully applied to solve path planning problems,but learning is generally *** main reason is not making full use of information collected during interaction with the *** paper proposes a novel method to solve the discrete space path planning problem in an environment without prior knowledge with intensive obstacles based on RL and heuristic ***,we apply Dyna-Q algorithm of RL to explore the map and search for the target point and optimize its policy with upper confidence bound(UCB).Then,when the target point is found,we use heuristic search to plan the path from the starting point to the target point and narrow the path to a small ***,we combine Dyna-Q algorithm with the heuristic search recommended path for path *** evaluate our algorithm using maze navigation *** results verify that heuristic search accelerates Dyan-Q convergence.

关键词： Dyna-Q algorithm Heuristic search Path planning Discrete space

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